Parallel hardware has been ubiquitous for some time now. It’s difficult to find a laptop,
desktop, or server that doesn’t use a multicore processor. Beowulf clusters are
nearly as common today as high-powered workstations were during the 1990s, and
cloud computing could make distributed-memory systems as accessible as desktops.
In spite of this, most computer science majors graduate with little or no experience in
parallel programming. Many colleges and universities offer upper-division elective
courses in parallel computing, but since most computer science majors have to take
numerous required courses, many graduate without ever writing a multithreaded or
multiprocess program.
It seems clear that this state of affairs needs to change. Although many programs
can obtain satisfactory performance on a single core, computer scientists should be
made aware of the potentially vast performance improvements that can be obtained
with parallelism, and they should be able to exploit this potential when the need
arises.
An Introduction to Parallel Programming was written to partially address this
problem. It provides an introduction to writing parallel programs using MPI,
Pthreads, and OpenMP—three of the most widely used application programming
interfaces (APIs) for parallel programming. The intended audience is students and
professionals who need to write parallel programs. The prerequisites are minimal:
a college-level course in mathematics and the ability to write serial programs
in C. They are minimal because we believe that students should be able to start
programming parallel systems as early as possible.
At the University of San Francisco, computer science students can fulfill a
requirement for the major by taking the course, on which this text is based, immediately
after taking the “Introduction to Computer Science I” course that most majors
take in the first semester of their freshman year. We’ve been offering this course
in parallel computing for six years now, and it has been our experience that there
really is no reason for students to defer writing parallel programs until their junior
or senior year. To the contrary, the course is popular, and students have found that
using concurrency in other courses is much easier after having taken the Introduction
course.
If second-semester freshmen can learn to write parallel programs by taking a
class, then motivated computing professionals should be able to learn to write parallel
programs through self-study.We hope this book will prove to be a useful resource
for them.
